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Dynamic Sampling for Visual Exploration of Large Dense-Dense Matrices

Roskosch, Philipp and Twellmeyer, James and Kuijper, Arjan (2016):
Dynamic Sampling for Visual Exploration of Large Dense-Dense Matrices.
In: Human Interface and the Management of Information: Information, Design and Interaction, Springer International Publishing, In: 18th International Conference, HCI International 2016, Toronto, Canada, July 17-22, 2016, In: Lecture Notes in Computer Science (LNCS); 9734, DOI: 10.1007/978-3-319-40349-6_29,
[Conference or Workshop Item]

Abstract

We present a technique which allows visual exploration of large dense-occupied similarity matrices. It allows the comparison of several dimensions of a multivariate data set. For the visualization, the data are reduced by sampling. The access time to individual elements is an ever increasing problem with increasing matrix size. We examine various database management systems and compare the access times for different problem sizes. The visualization responds to user interaction and allows the focus to specific areas within the data. For this, the data is filtered according to user interests and the visualization is refined with subsamples of the filtered data. The context is preserved in this process. The focus allows the discovery of relationships that would otherwise remain hidden.

Item Type: Conference or Workshop Item
Erschienen: 2016
Creators: Roskosch, Philipp and Twellmeyer, James and Kuijper, Arjan
Title: Dynamic Sampling for Visual Exploration of Large Dense-Dense Matrices
Language: English
Abstract:

We present a technique which allows visual exploration of large dense-occupied similarity matrices. It allows the comparison of several dimensions of a multivariate data set. For the visualization, the data are reduced by sampling. The access time to individual elements is an ever increasing problem with increasing matrix size. We examine various database management systems and compare the access times for different problem sizes. The visualization responds to user interaction and allows the focus to specific areas within the data. For this, the data is filtered according to user interests and the visualization is refined with subsamples of the filtered data. The context is preserved in this process. The focus allows the discovery of relationships that would otherwise remain hidden.

Title of Book: Human Interface and the Management of Information: Information, Design and Interaction
Series Name: Lecture Notes in Computer Science (LNCS); 9734
Publisher: Springer International Publishing
Uncontrolled Keywords: Guiding Theme: Digitized Work, Research Area: Human computer interaction (HCI), Computer security, Network visualization, Sampling, Visual analytics, Matrix representation
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: 18th International Conference, HCI International 2016
Event Location: Toronto, Canada
Event Dates: July 17-22, 2016
Date Deposited: 06 May 2019 06:55
DOI: 10.1007/978-3-319-40349-6_29
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